In the last five years, Transformer models [1] have become the de facto standard for many machine learning (ML) tasks, such as natural language processing (NLP), computer vision (CV), speech, and more. Today, many data scientists and    

Did you know you can host a Unity game in a Hugging Face Space? No? Well, you can! Hugging Face Spaces are an easy way to build, host, and share demos. While they are typically used for Machine Learning demos, they can also host playable Unity games. Here are some examples: Here’s how you can host your own Unity game in a    

We are delighted to introduce Hugging Face’s new blog for Chinese speakers: hf.co/blog/zh! A committed group of volunteers has made this possible by translating our invaluable resources, including blog posts and comprehensive courses on transformers, diffusion, and reinforcement learning. This step aims to make our content accessible to the ever-growing Chinese AI community, fostering mutual learning and collaboration. Recognizing the Chinese AI Community’s Accomplishments We want to highlight the remarkable achievements and contributions of the Chinese AI community, which has […]

Generative AI has been taking the world by storm. As the data and AI company, we have been on this journey with the release of the open source large language model Dolly, as well as the internally crowdsourced    

TL;DR: We show how to run one of the most powerful open-source text to image models IF on a free-tier Google Colab with 🧨 diffusers. You can also explore the capabilities of the model directly in the Hugging Face Space. Image compressed from official IF GitHub repo. Introduction IF is a pixel-based text-to-image generation model and was released in late    

The Hugging Face Unity API is an easy-to-use integration of the Hugging Face Inference API, allowing developers to access and use Hugging Face AI models in their Unity projects. In this blog post, we’ll walk through the steps to install and use the Hugging Face Unity API.

StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained    

Video samples generated with ModelScope. Text-to-video is next in line in the long list of incredible advances in generative models. As self-descriptive as it is, text-to-video is a fairly new computer vision task that involves generating a sequence of images from text descriptions that are both temporally and spatially consistent. While this task might seem